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Seanium_Brain.py
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Seanium_Brain.py
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import os
import streamlit as st
import streamlit_toggle as st_toggle
import modules.INFO as INFO
import modules as mod
import GPT
import modules.utilities as util
import streamlit_toolkit.tools as st_tool
SESSION_TIME = st.session_state['SESSION_TIME']
SESSION_LANG = st.session_state['SESSION_LANGUAGE']
PROMPT_PATH = f'.user/prompt/{SESSION_LANG}'
CURRENT_LOG_FILE = f'{INFO.LOG_PATH}/log_{SESSION_TIME}.log'
util.remove_oldest_file(INFO.LOG_PATH, 10)
header = st.container()
body = st.container()
# sidebar
with st.sidebar:
_ = mod.language.set_language()
st.title(_('Settings'))
mod.language.select_language()
prompt_files = util.scan_directory(PROMPT_PATH)
prompt_file_names = [util.get_file_name(file) for file in prompt_files]
prompt_dictionary = dict(zip(prompt_file_names, prompt_files))
# remove 'my-info' from prompt dictionary
prompt_dictionary.pop(_('my-info'))
operation_options = list(prompt_dictionary.keys())
operations = st.multiselect(_('Operations'),
operation_options,
default=util.read_json_at(INFO.BRAIN_MEMO, f'operations_{SESSION_LANG}',
_('question')),
help=_('Combinations of operations to perform.'))
last_question_model = util.read_json_at(INFO.BRAIN_MEMO, 'question_model', INFO.MODELS_OPTIONS[0])
# get index of last question model
question_model_index = util.get_index(INFO.MODELS_OPTIONS, last_question_model)
question_model = st.selectbox(_('Question Model'), INFO.MODELS_OPTIONS, index=question_model_index,
help=_('Model used for answering user question.'))
operations_no_question = [op for op in operations if op != _('question')]
other_models = []
replace_tokens = []
for operation in operations_no_question:
last_model = util.read_json_at(INFO.BRAIN_MEMO, f'{operation}_model', INFO.MODELS_OPTIONS[0])
# get index of last model
model_index = util.get_index(INFO.MODELS_OPTIONS, last_model)
model = st.selectbox(f"{operation} " + _('Model'), INFO.MODELS_OPTIONS, index=model_index)
other_models.append(model)
temp = st.slider(_('Temperature'), 0.0, 1.0, value=util.read_json_at(INFO.BRAIN_MEMO, 'temp', 0.1),
help=_('What sampling temperature to use, between 0 and 1. Higher values like 0.8 will make the '
'output more random, while lower values like 0.2 will make it more focused and '
'deterministic. \n\nIt is generally recommend altering this or `top_p` but not both.'))
max_tokens = st.slider(_('Max Tokens'), 10, 4096, value=util.read_json_at(INFO.BRAIN_MEMO, 'max_tokens', 1000),
help=_("The maximum number of tokens to generate in the completion.\n\nThe token count of "
"your prompt plus `max_tokens` cannot exceed the model's context length. Most "
"models have a context length of 2048 tokens (except for the newest models, "
"which support 4096)."))
col1, col2 = st.columns([3, 1])
with col1:
chunk_size = st.slider(_('Chunk size'), 1500, 4500,
value=util.read_json_at(INFO.BRAIN_MEMO, 'chunk_size', 4000),
help=_("The number of tokens to consider at each step. The larger this is, the more "
"context the model has to work with, but the slower generation and expensive "
"will it be."))
with col2:
update_brain = st.button(_('Update Brain'))
with st.expander(label=_('Advanced Options')):
top_p = st.slider(_('Top_P'), 0.0, 1.0, value=util.read_json_at(INFO.BRAIN_MEMO, 'top_p', 1.0),
help=_("An alternative to sampling with temperature, called nucleus sampling, where the "
"model considers the results of the tokens with top_p probability mass. So 0.1 means "
"only the tokens comprising the top 10% probability mass are considered.\n\n"
"It is generally recommend altering this or `temperature` but not both."))
freq_panl = st.slider(_('Frequency penalty'), 0.0, 2.0,
value=util.read_json_at(INFO.BRAIN_MEMO, 'frequency_penalty', 0.0),
help=_("Larger the number increasing the model's likelihood to talk about new topics. "
"Penalize new tokens based on whether they appear in the text so far."
"\n\n[See more information about frequency and presence penalties.]"
"(https://platform.openai.com/docs/api-reference/parameter-details)"))
pres_panl = st.slider(_('Presence penalty'), 0.0, 1.0,
value=util.read_json_at(INFO.BRAIN_MEMO, 'present_penalty', 0.0),
help=_("Decreasing the model's likelihood to repeat the same line verbatim. Penalize "
"new tokens based on their existing frequency in the text so far."
"\n\n[See more information about frequency and presence penalties.]"
"(https://platform.openai.com/docs/api-reference/parameter-details)"))
enable_stream = st_toggle.st_toggle_switch(_('Stream (experimental)'),
default_value=util.read_json_at(INFO.BRAIN_MEMO, 'enable_stream',
True))
if not enable_stream:
chunk_count = st.slider(_('Answer count'), 1, 5, value=util.read_json_at(INFO.BRAIN_MEMO, 'chunk_count', 1),
help=_("The number of answers to generate. The model will continue to iteratively "
"generating answers until it reaches the answer count."
"\n\nNote that this function does not supports `stream` mode."))
else:
chunk_count = 1
param = GPT.model.param(temp=temp,
max_tokens=max_tokens,
top_p=top_p,
frequency_penalty=freq_panl,
present_penalty=pres_panl,
chunk_size=chunk_size,
chunk_count=chunk_count)
op = GPT.model.Operation(operations=operations,
operations_no_question=operations_no_question)
models = GPT.model.Model(question_model=question_model,
other_models=other_models)
prompt_core = GPT.model.prompt_core(question=f'{PROMPT_PATH}/' + _('question') + '.txt',
my_info=f'{PROMPT_PATH}/' + _('my-info') + '.txt')
if st.button(_('Clear Log'), on_click=st_tool.clear_log):
st.success(_('Log Cleared'))
# info
st.markdown('---')
st.markdown(f"# {util.read_json_at(INFO.MANIFEST, 'name')}")
st.markdown(_('Version') + f": {util.read_json_at(INFO.MANIFEST, 'version')}")
st.markdown(_('Author') + f": {util.read_json_at(INFO.MANIFEST, 'author')}")
st.markdown("[" + _('Report bugs') + "]" + f"({util.read_json_at(INFO.MANIFEST, 'bugs')})")
st.markdown("[" + _('Github Repo') + "]" + f"({util.read_json_at(INFO.MANIFEST, 'homepage')})")
with header:
st.title(_('🧠GPT-Brain'))
st.text(_('This is my personal AI powered brain feeding my own Obsidian notes. Ask anything.'))
st_tool.message(_("This is a beta version. Please [🪲report bugs](") +
util.read_json_at(INFO.MANIFEST, 'bugs') + _(") if you find any."))
# main
with body:
query = st.text_area(_('Ask Brain: '))
col1, col2 = st.columns([1, 3])
with col1:
send = st.button(_('📩Send'))
with col2:
if os.path.exists(CURRENT_LOG_FILE):
st_tool.download_as(_("📥download log"))
# execute brain calculation
if update_brain:
st_tool.rebuild_brain(chunk_size)
if not query == '':
if models.question_model == 'text-davinci-003' or 'text-davinci-003' in models.other_models:
max_model_token = 4096
elif models.question_model == 'gpt-3.5-turbo' or 'gpt-3.5-turbo' in models.other_models:
max_model_token = 4096
else:
max_model_token = 2048
tokens, isTokenZero = st_tool.predict_token(query, prompt_core)
token_panel = st.empty()
if isTokenZero:
token_panel.markdown('Prompt token: `Not Available`')
else:
token_panel.markdown(f'Prompt token: `{tokens}/{max_model_token}`')
if send:
st_tool.execute_brain(query,
param,
op,
models,
prompt_core,
prompt_dictionary,
_('question'),
enable_stream)
# convert param to dictionary
param_dict = vars(param)
# write param to json
for key in param_dict:
value = param_dict[key]
util.update_json(INFO.BRAIN_MEMO, key, value)
# write operation to json
util.update_json(INFO.BRAIN_MEMO, f'operations_{SESSION_LANG}', op.operations)
# write question model to json
util.update_json(INFO.BRAIN_MEMO, 'question_model', models.question_model)
# write other models to json
for i in range(len(op.operations_no_question)):
util.update_json(INFO.BRAIN_MEMO, f'{op.operations_no_question[i]}_model', models.other_models[i])
# write stream to json
util.update_json(INFO.BRAIN_MEMO, 'enable_stream', enable_stream)